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Challenges and Strategies in Cross-Cultural NLP

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2022.acl-long.482.pdf

Summary

This position and survey paper argues that NLP systems that serve speakers worldwide must adapt to culture as well as to language. The two are related but different: a single language can include many cultures, and a fairly uniform culture can span several languages. The authors propose a framework of four dimensions on which cultures vary and on which systems may show cultural bias:

  • the form and style of language;
  • common ground (shared concepts and commonsense knowledge);
  • aboutness (which topics people find worth discussing);
  • objectives or values.

For each dimension, they collect evidence from linguistics, anthropology and NLP. They then review strategies for closing these gaps, covering data collection, model training and adaptation, and translation that adapts content for a target culture. The paper introduces no new dataset or model. Its contribution is a taxonomy and a research agenda. This matters because cultural mismatch can cause misinterpretation and real harm, such as misdiagnosing depression. The authors call for participatory work with communities, culture-aware human evaluation, and decolonial approaches that let non-WEIRD groups set their own priorities.

Key points

  • Within-language variation is often ignored. The Spanish PLM of Cañete et al. (2020) does not distinguish Spain from Argentina. In contrast, a PLM built for the Algerian Arabic dialect beat multilingual and general Arabic models on sentiment classification.
  • In the MarVL dataset (Liu et al., 2021), roughly one quarter of concepts drawn from non-Western cultures had no counterpart among English concepts in WordNet.
  • Ringel et al. (2019) used cultural differences as distant supervision. They derived English formality labels from German and sarcasm labels from Japanese.
  • Common NLP domains are culture-specific. Beer reviews in sentiment analysis are one example, and image-based tasks show a North American and Western European bias.
  • Two strategies for cross-cultural translation are discussed. Peskov et al. (2021) replace entities with rough equivalents from the target culture, while Kementchedjhieva et al. (2020) add explanatory appositives. Reference-based automatic metrics are considered unreliable for this kind of translation.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

194 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 187 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.acl-long.482

@inproceedings{hershcovich2022challenges,
  title = {{Challenges and Strategies in Cross-Cultural NLP}},
  author = {Hershcovich, Daniel and Frank, Stella and Lent, Heather and Lhoneux, Miryam de and Abdou, Mostafa and Brandl, Stephanie and Bugliarello, Emanuele and Piqueras, Laura Cabello and Chalkidis, Ilias and Cui, Ruixiang and Fierro, Constanza and Margatina, Katerina and Rust, Phillip and Søgaard, Anders},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.482},
  url = {https://doi.org/10.18653/v1/2022.acl-long.482}
}

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